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Column

Language groups

Musical sophistication (Gold MSI score) vs average sentiment of the lyrics from the OnRepeat playlist

All languages

Musical sophistication (Gold MSI score) vs average sentiment of the lyrics from the On Repeat playlist

Column

Lyrics readability vs the English proficiency

Countries of study participants

The Participants

Row

Overall

92

Number

55

Countries

16

Languages

15

Average Gold

78.6

Row

Languages in the study

Frequency ranking of artists included in the study

Main_artist Count Rank
Taylor Swift 26 1
Hozier 18 2
Noah Kahan 14 3
Lana Del Rey 11 4
Olivia Rodrigo 10 5
Måneskin 9 6
Giant Rooks 8 7
Gracie Abrams 8 8
Ben Howard 7 9
Charli XCX 7 10
Dua Lipa 7 11
Kendrick Lamar 7 12
Mindless Self Indulgence 7 13
Pulp 7 14
Arctic Monkeys 6 15
Dominic Fike 6 16
Laufey 6 17
Tate McRae 6 18
The Beatles 6 19
21 Savage 5 20
Adele 5 21
Daryl Hall & John Oates 5 22
Jack Harlow 5 23
Loyle Carner 5 24
Madonna 5 25
Rihanna 5 26
The Hives 5 27
Timothée Chalamet 5 28
yeule 5 29
ABBA 4 30
Bastille 4 31
Billy Joel 4 32
Brent Faiyaz 4 33
Bruno Mars 4 34
Coldplay 4 35
Daft Punk 4 36
Ed Sheeran 4 37
Fred again.. 4 38
Jungle 4 39
Kanye West 4 40
Lewis Capaldi 4 41
Mitski 4 42
Offset 4 43
Slowdive 4 44
The Strokes 4 45
The Weeknd 4 46
Toby Keith 4 47
Tyler 4 48
5 Seconds of Summer 3 49
Alice Phoebe Lou 3 50
Amy Macdonald 3 51
Avishai Cohen 3 52
Benny The Butcher 3 53
Chet Faker 3 54
Conway the Machine 3 55
Dayglow 3 56
Ecco2k 3 57
Frank Ocean 3 58
Harry Styles 3 59
JAWNY 3 60
Kenya Grace 3 61
Lady Gaga 3 62
League of Legends 3 63
Lizzy McAlpine 3 64
lostrushi 3 65
Melanie Martinez 3 66
Metro Boomin 3 67
Minnz Piano 3 68
Niall Horan 3 69
Post Malone 3 70
ROME 3 71
Ryan Gosling 3 72
Ryder 3 73
Skepta 3 74
Sophie Ellis-Bextor 3 75
The Damned 3 76
The Mamas & The Papas 3 77
the pillows 3 78
The Rapture 3 79
Tō Yō 3 80
Troye Sivan 3 81
6LACK 2 82
almost monday 2 83
Ashe 2 84
Ava Max 2 85
AZALI 2 86
Bear McCreary 2 87
Bee Gees 2 88
Besomage 2 89
Besomorph 2 90
Billie Eilish 2 91
Birdy 2 92
Black Eyed Peas 2 93
Blondie 2 94
cassö 2 95
Chase Atlantic 2 96
Childish Gambino 2 97
Dave 2 98
Doja Cat 2 99
Drake 2 100
EFESIAN 2 101
ENNY 2 102
Fana Hues 2 103
Fleetwood Mac 2 104
HARDSTYLE MAGE 2 105
Hugh Grant 2 106
iamnotshane 2 107
Jackson Marshall 2 108
Joji 2 109
José González 2 110
Judas Priest 2 111
Justin Timberlake 2 112
Katy Perry 2 113
KAYTRANADA 2 114
Kid Cudi 2 115
KISS 2 116
KUZKO 2 117
Kwaku Asante 2 118
Kygo 2 119
Kylie Minogue 2 120
Lennon Stella 2 121
Lil Nas X 2 122
Lil Uzi Vert 2 123
Lilly Wood and The Prick 2 124
Little Simz 2 125
Lucid Monday 2 126
Mac Miller 2 127
Madcon 2 128
MARINA 2 129
Marvin Gaye 2 130
Miley Cyrus 2 131
Nines 2 132
Olly Alexander (Years & Years) 2 133
Omar Apollo 2 134
P!nk 2 135
Paul Simon 2 136
Pitbull 2 137
PJ Harding 2 138
Radiohead 2 139
RAYE 2 140
Remi Wolf 2 141
Robbie Williams 2 142
Sabrina Carpenter 2 143
SALES 2 144
Shakira 2 145
Sia 2 146
Snow Patrol 2 147
Suki Waterhouse 2 148
Tame Impala 2 149
Tears For Fears 2 150
The Kount 2 151
The Longest Johns 2 152
The Neighbourhood 2 153
The Smile 2 154
Timbaland 2 155
Tobÿ 2 156
Tom Misch 2 157
VALORANT 2 158
Victoria Monét 2 159
Violent Femmes 2 160
ZAYN 2 161
¥ellow Bucks 1 162
¥S 1 163
24. Avēnija 1 164
2Scratch 1 165
49 Winchester 1 166
8ruki 1 167
A1 x J1 1 168
Absolute Valentine 1 169
AC/DC 1 170
Aden Foyer 1 171
Ado 1 172
Air 1 173
Aitch 1 174
Aiyana-Lee 1 175
AJ Tracey 1 176
Akon 1 177
Alexander 23 1 178
Aliyah’s Interlude 1 179
alt-J 1 180
Alvvays 1 181
Aly & AJ 1 182
America 1 183
Aminé 1 184
Anastacia 1 185
Andrew Lambrou 1 186
Arcade Fire 1 187
ASAP Ferg 1 188
Ashbeck 1 189
ASKII 1 190
Aslak Trude 1 191
Astrud Gilberto 1 192
Austin Farwell 1 193
Avi Kaplan 1 194
AYYBO 1 195
Babe Rainbow 1 196
Baby Keem 1 197
Barbra Streisand 1 198
Barney Artist 1 199
Barry Can’t Swim 1 200
Bathe 1 201
Bauhaus 1 202
Beach Weather 1 203
Ben Platt 1 204
benny blanco 1 205
Benny Sings 1 206
berlioz 1 207
Bérurier Noir 1 208
Big Thief 1 209
BLK ODYSSY 1 210
Blue Öyster Cult 1 211
Bob Marley & The Wailers 1 212
Bobby Womack 1 213
Bon Iver 1 214
Bon Jovi 1 215
Bonobo 1 216
Boostereo 1 217
boy pablo 1 218
boygenius 1 219
Boys Town Gang 1 220
Britney Spears 1 221
BROCKHAMPTON 1 222
Bruce Springsteen 1 223
BTS 1 224
Cage The Elephant 1 225
CAKE 1 226
Calah Lane 1 227
Cardi B 1 228
Carpenters 1 229
Celeste 1 230
Central Cee 1 231
Certified Trapper 1 232
Chance Peña 1 233
Charles Aznavour 1 234
Cherry Pit 1 235
Chikinki 1 236
Chopstick Sisters 1 237
Chris Brown 1 238
Chris Isaak 1 239
Chris Young 1 240
Christian Reindl 1 241
Cigarettes After Sex 1 242
Cody Chesnutt 1 243
Cold Dew 1 244
Combichrist 1 245
Creedence Clearwater Revival 1 246
CRO 1 247
Crystal Castles 1 248
Current Blue 1 249
Daniel Caesar 1 250
Daniel Di Angelo 1 251
Dao Bandon 1 252
David Bowie 1 253
David Guetta 1 254
David Kushner 1 255
DC 1 256
DECAP 1 257
Declan McKenna 1 258
Deep Chills 1 259
Depeche Mode 1 260
Desiigner 1 261
dialE 1 262
Dido 1 263
Disclosure 1 264
dj gummy bear 1 265
Dotan 1 266
Duran Duran 1 267
Dusty Douglas 1 268
Earth 1 269
EARTHGANG 1 270
Edwin Starr 1 271
ELIO 1 272
Ellie Goulding 1 273
Elton John 1 274
Embryo 1 275
EMELINE 1 276
Emigrate 1 277
Emilíana Torrini 1 278
Eminem 1 279
Empire of the Sun 1 280
Enzo Ingrosso 1 281
Evan Yo 1 282
Father John Misty 1 283
Felix Jaehn 1 284
Fergie 1 285
FKA twigs 1 286
Florence + The Machine 1 287
Flozigg 1 288
Flume 1 289
Foals 1 290
forcian 1 291
Foreign Air 1 292
Frank Sinatra 1 293
Full Crate 1 294
fun. 1 295
Gary Wolk 1 296
Gengahr 1 297
George Michael 1 298
GG Magree 1 299
girl in red 1 300
Gotts Street Park 1 301
Greta Van Fleet 1 302
GROUPLOVE 1 303
Guns N’ Roses 1 304
Gym Class Heroes 1 305
Haley Heynderickx 1 306
Halsey 1 307
Hannah Laing 1 308
Harold Budd 1 309
HAZEY 1 310
HEARTSTEEL 1 311
Henning Nugel 1 312
Henry And The Waiter 1 313
Hi-C 1 314
Hiroyuki Sawano 1 315
Hodak 1 316
Hotel Ugly 1 317
Hugh Jackman 1 318
HUNNY 1 319
Ichiko Aoba 1 320
Imagine Dragons 1 321
INJI 1 322
Interpol 1 323
Invadable Harmony 1 324
Irene Cara 1 325
Isabel LaRosa 1 326
Israel Kamakawiwo’ole 1 327
J. Maya 1 328
J.J. Cale 1 329
jabeau 1 330
Jack Conte 1 331
Jack Johnson 1 332
Jackie Gleason 1 333
Jacob Banks 1 334
Jacob Collier 1 335
Jagwar Twin 1 336
Jake Wesley Rogers 1 337
Jan Valta 1 338
Jaz Karis 1 339
Jefferson Airplane 1 340
Jerry Folk 1 341
Jeshi 1 342
Jessie J 1 343
jibun 1 344
Jimi Hendrix 1 345
Jimmie Allen 1 346
Jme 1 347
Joel Corry 1 348
Joesef 1 349
Jonas Brothers 1 350
Joni Mitchell 1 351
Jordiz 1 352
Jorge Rivera-Herrans 1 353
Jorja Smith 1 354
Joy Crookes 1 355
Juice WRLD 1 356
Julee Cruise 1 357
JUNIEL 1 358
Junip 1 359
Jupiter 1 360
jus. 1 361
JVKE 1 362
Kaleido Young 1 363
KALEO 1 364
Kali Uchis 1 365
Kane Brown 1 366
Katie Gregson-MacLeod 1 367
Kelly Clarkson 1 368
Kholby Wardell 1 369
Kid Francescoli 1 370
King Gizzard & The Lizard Wizard 1 371
Knxwledge 1 372
KT Tunstall 1 373
Kungs 1 374
Kylie Morgan 1 375
LAUSSE THE CAT 1 376
Lazerhawk 1 377
Leah Kate 1 378
Lee Clarke 1 379
Leif Vollebekk 1 380
Lenka 1 381
Leo Mano 1 382
Leslie Odom Jr. 1 383
Lily Allen 1 384
LiSA 1 385
Los Sospechos 1 386
Lost Frequencies 1 387
LP 1 388
lucidbeatz 1 389
LUCKY LOVE 1 390
Lucy Dacus 1 391
Luke Bryan 1 392
Macco 1 393
Macklemore & Ryan Lewis 1 394
Maffio 1 395
MAGIC! 1 396
MALARKEY 1 397
Malik Montana 1 398
Manchester United Football Team 1 399
Manu Chao 1 400
Mark Ronson 1 401
Marshmello 1 402
Masego 1 403
Matteo Tura 1 404
MATVEÏ 1 405
Maxo Kream 1 406
maya ongaku 1 407
Men At Work 1 408
Metallica 1 409
Michael Jackson 1 410
Michael Kiwanuka 1 411
Mike WiLL Made-It 1 412
Miniature Tigers 1 413
Miracle Of Sound 1 414
Modern Talking 1 415
Moloko 1 416
Monster High 1 417
Montell Fish 1 418
Mumford & Sons 1 419
My Marianne 1 420
N.L.T. 1 421
Nancy Sinatra 1 422
Nat King Cole 1 423
Natasha Bedingfield 1 424
NEIL FRANCES 1 425
Nelly Furtado 1 426
Nemzzz 1 427
Nessa Barrett 1 428
Nettspend 1 429
Nia Archives 1 430
Nick Mulvey 1 431
Nickelback 1 432
Nicki Minaj 1 433
Nico & Vinz 1 434
Nightwish 1 435
Nirvana 1 436
Novo Amor 1 437
Oasis 1 438
Ochman 1 439
Ocie Elliott 1 440
ODESZA 1 441
Odium 1 442
Olivia Dean 1 443
Olivia Newton-John 1 444
Olivia O’Brien 1 445
One Direction 1 446
ONE OK ROCK 1 447
OneRepublic 1 448
Ostava 1 449
Other Nothing 1 450
Ouska 1 451
Pale Jay 1 452
Panic! At The Disco 1 453
Parcels 1 454
Parliament Funkadelic 1 455
Paterson Joseph 1 456
Paul Russell 1 457
PawPaw Rod 1 458
Perturbator 1 459
Peyton Parrish 1 460
Phantogram 1 461
Phoebe Bridgers 1 462
Pink SweatS 1 463
Playboi Carti 1 464
Player 1 465
Pritam 1 466
Queen 1 467
R.E.M. 1 468
Rachel Hardy 1 469
Rag’n’Bone Man 1 470
Ramon Mirabet 1 471
Ray Fuego 1 472
Rayana Jay 1 473
Reamonn 1 474
Red Hot Chili Peppers 1 475
Rejjie Snow 1 476
Rema 1 477
Reneé Rapp 1 478
Rikon Nozaki 1 479
Russ 1 480
Ryan Caraveo 1 481
S1mba 1 482
Sabrina Claudio 1 483
Sade 1 484
SAINt JHN 1 485
Sainté 1 486
Sam Smith 1 487
Sarah Kang 1 488
SawanoHiroyuki[nZk] 1 489
Scorpions 1 490
Sean Paul 1 491
Sébastien Tellier 1 492
Selena Gomez 1 493
SG Lewis 1 494
Shawn Mendes 1 495
Sheryl Crow 1 496
Simian Mobile Disco 1 497
simon robert french 1 498
Snoh Aalegra 1 499
Spoko Loko 1 500
Square Perception 1 501
St. Lundi 1 502
Stenli 1 503
Stephanie Economou 1 504
Stereolab 1 505
Stereophonics 1 506
Steve Lacy 1 507
Still Woozy 1 508
Sting 1 509
Sudden Lights 1 510
Sudley 1 511
Sunni Colón 1 512
Supergrass 1 513
Surf Curse 1 514
Tai Verdes 1 515
Technotronic 1 516
Teckill 1 517
The Animals 1 518
The Beach Boys 1 519
The Buttress 1 520
The Cast of Wonka 1 521
The Charlie Daniels Band 1 522
The Chemical Brothers 1 523
The Clash 1 524
The Cranberries 1 525
The Crystals 1 526
The Cure 1 527
The Fratellis 1 528
The HU 1 529
The Internet 1 530
The Kid LAROI 1 531
The Killers 1 532
The Kooks 1 533
The Libertines 1 534
The Lumineers 1 535
The Pogues 1 536
The Revivalists 1 537
The Righteous Brothers 1 538
The Ronettes 1 539
The Smiths 1 540
The Sound 1 541
The Spinners 1 542
The Streets 1 543
The The 1 544
The Tiger Lillies 1 545
The Virginmarys 1 546
The Wake 1 547
The War On Drugs 1 548
The Whitest Boy Alive 1 549
Thundercat 1 550
ThxSoMch 1 551
Tiësto 1 552
Tiffany Tatreau 1 553
Tinashe 1 554
Tired Pony 1 555
Tom Cardy 1 556
Tom Odell 1 557
Tom Paxton 1 558
Tom Rosenthal 1 559
TOMORROW X TOGETHER 1 560
Toploader 1 561
TOPS 1 562
Tory Lanez 1 563
Train 1 564
Travis Scott 1 565
Turno 1 566
Two Another 1 567
Uffie 1 568
Utada 1 569
Vance Joy 1 570
Vulfpeck 1 571
Wabie 1 572
Walterwarm 1 573
Warin Shinaraj 1 574
Wasia Project 1 575
Weston Estate 1 576
Westside Gunn 1 577
Wham! 1 578
Whitney Houston 1 579
Widowspeak 1 580
WILLOW 1 581
Wire 1 582
Wolf Alice 1 583
Yaya Bey 1 584
Yeah Yeah Yeahs 1 585
Young Marble Giants 1 586
Younger Hunger 1 587
your best friend jippy 1 588
Zedd 1 589
Zeph 1 590
Zergananda 1 591
Zhané 1 592
フレネシ 1 593

Frequency ranking of all artists in the playlists

Main_artist Count Rank
Taylor Swift 27 1
PRO8L3M 20 2
Hozier 18 3
Noah Kahan 16 4
Måneskin 14 5
Olivia Rodrigo 13 6
Lana Del Rey 12 7
Charli XCX 8 8
Giant Rooks 8 9
Gracie Abrams 8 10
Jetlagz 8 11
Kenshi Yonezu 8 12
Minnz Piano 8 13
フレネシ 8 14
Bear McCreary 7 15
Ben Howard 7 16
Daft Punk 7 17
Dua Lipa 7 18
Eviatar Banai 7 19
Kendrick Lamar 7 20
Mindless Self Indulgence 7 21
Pulp 7 22
Taco Hemingway 7 23
時代少年團 7 24
Arctic Monkeys 6 25
Dominic Fike 6 26
Laufey 6 27
schafter 6 28
Stupeflip 6 29
Tate McRae 6 30
The Beatles 6 31
Timothée Chalamet 6 32
yeule 6 33
21 Savage 5 34
Adele 5 35
Avishai Cohen 5 36
bambi 5 37
Besomage 5 38
Daryl Hall & John Oates 5 39
Drake 5 40
Ed Sheeran 5 41
Jack Harlow 5 42
Kukon 5 43
L.U.C. 5 44
Lady Gaga 5 45
Loyle Carner 5 46
Madonna 5 47
Miś i Margolcia 5 48
Rammstein 5 49
ReTo 5 50
Rihanna 5 51
Śpiewanki 5 52
Tatsuya Kitani 5 53
The Hives 5 54
The Weeknd 5 55
ABBA 4 56
Bastille 4 57
Billy Joel 4 58
Brent Faiyaz 4 59
Bruno Mars 4 60
Coldplay 4 61
Frank Ocean 4 62
Fred again.. 4 63
Harry Styles 4 64
Hermanos Gutiérrez 4 65
Jungle 4 66
Kanaria 4 67
Kanye West 4 68
Lewis Capaldi 4 69
MAISONdes 4 70
Mitski 4 71
MuKuRo 4 72
Offset 4 73
Oscar 4 74
Pashanim 4 75
Pezet 4 76
Slowdive 4 77
The Rapture 4 78
The Strokes 4 79
Tō Yō 4 80
Toby Keith 4 81
Tyler 4 82
Vibe Ish 4 83
5 Seconds of Summer 3 84
Acda en de Munnik 3 85
Alice Phoebe Lou 3 86
Amy Macdonald 3 87
Bad Bunny 3 88
Benny The Butcher 3 89
Bizarrap 3 90
Calcutta 3 91
Charles Aznavour 3 92
Chet Faker 3 93
Chivas 3 94
Conway the Machine 3 95
CRO 3 96
David Arkenstone 3 97
Dawid Podsiadło 3 98
Dayglow 3 99
Dudu Tassa 3 100
Ecco2k 3 101
Frah Quintale 3 102
Gemitaiz 3 103
HAŁASTRA 3 104
Hesham Abdul Wahab 3 105
Hiroyuki Sawano 3 106
Itzo Hazarta 3 107
JAWNY 3 108
Judas Priest 3 109
Kenya Grace 3 110
League of Legends 3 111
Lizzy McAlpine 3 112
lostrushi 3 113
Melanie Martinez 3 114
Metro Boomin 3 115
Niall Horan 3 116
Oki 3 117
Pino D’Angiò 3 118
Post Malone 3 119
Quevedo 3 120
Rokiczanka 3 121
ROME 3 122
Ryan Gosling 3 123
Ryder 3 124
Skepta 3 125
Sophie Ellis-Bextor 3 126
TACONAFIDE 3 127
The Damned 3 128
The Mamas & The Papas 3 129
the pillows 3 130
Troye Sivan 3 131
YOASOBI 3 132
ヨルシカ 3 133
6LACK 2 134
8ruki 2 135
Ado 2 136
Alma Gov 2 137
almost monday 2 138
ANIKV 2 139
Anirudh Ravichander 2 140
AnnenMayKantereit 2 141
Ashe 2 142
Astrud Gilberto 2 143
Ava Max 2 144
Avi 2 145
Ayase 2 146
AZALI 2 147
Bakermat 2 148
Barry Can’t Swim 2 149
Bauhaus 2 150
Bee Gees 2 151
Besomorph 2 152
Billie Eilish 2 153
Birdy 2 154
Black Eyed Peas 2 155
Blondie 2 156
Boris Vian 2 157
cassö 2 158
Chase Atlantic 2 159
Childish Gambino 2 160
Coez 2 161
Creepy Nuts 2 162
Daniele Silvestri 2 163
Dave 2 164
Die Ärzte 2 165
Doja Cat 2 166
EFESIAN 2 167
EGOIST 2 168
Emilíana Torrini 2 169
ENNY 2 170
Eve 2 171
Fana Hues 2 172
FKA twigs 2 173
Fleetwood Mac 2 174
Frank Sinatra 2 175
Gedz 2 176
Giga 2 177
Guzior 2 178
Habiluim 2 179
HARDSTYLE MAGE 2 180
Harry Gregson-Williams 2 181
Hikaru Utada 2 182
Hodak 2 183
Hugh Grant 2 184
iamnotshane 2 185
Ikkimel 2 186
imase 2 187
Jackson Marshall 2 188
Jluch 2 189
Joji 2 190
José González 2 191
Justin Timberlake 2 192
Kali Uchis 2 193
KALIM 2 194
KANKAN 2 195
KAROL G 2 196
Katy Perry 2 197
KAYTRANADA 2 198
Kelly Clarkson 2 199
Kid Cudi 2 200
KISS 2 201
ksiaze700 2 202
KUZKO 2 203
Kwaku Asante 2 204
Kygo 2 205
Kylie Minogue 2 206
Lennon Stella 2 207
Lil Nas X 2 208
Lil Uzi Vert 2 209
Lilly Wood and The Prick 2 210
Lindemann 2 211
LiSA 2 212
Little Simz 2 213
Luciano 2 214
Lucid Monday 2 215
Luke Bryan 2 216
Mac Miller 2 217
Madcon 2 218
Malik Montana 2 219
MARINA 2 220
Marshmello 2 221
Marvin Gaye 2 222
Mata 2 223
Męskie Granie Orkiestra 2 224
Miley Cyrus 2 225
Miuosh 2 226
natori 2 227
NEEDY GIRL OVERDOSE 2 228
Nines 2 229
Nitai Hershkovits 2 230
Olek030 2 231
Olly Alexander (Years & Years) 2 232
Omar Apollo 2 233
P!nk 2 234
Paul Simon 2 235
Pitbull 2 236
PJ Harding 2 237
Plan B 2 238
QUEEN BEE 2 239
Radiohead 2 240
RAYE 2 241
Rei Yasuda 2 242
Remi Wolf 2 243
Reol 2 244
Rikon Nozaki 2 245
Robbie Williams 2 246
ROSALÍA 2 247
Sabrina Carpenter 2 248
Sachin-Jigar 2 249
SALES 2 250
Shakira 2 251
Sia 2 252
Snoh Aalegra 2 253
Snow Patrol 2 254
Sobel 2 255
Somjai Nilbarun 2 256
Suki Waterhouse 2 257
Taiu 2 258
Tame Impala 2 259
Tears For Fears 2 260
The Cranberries 2 261
The Kount 2 262
The Longest Johns 2 263
The Neighbourhood 2 264
The Smile 2 265
Timbaland 2 266
Tobÿ 2 267
Tom Misch 2 268
Tymek 2 269
UMETORA 2 270
VALORANT 2 271
Victoria Monét 2 272
Videoclub 2 273
Violent Femmes 2 274
Wardruna 2 275
Yeah Yeah Yeahs 2 276
Yehudit Ravitz 2 277
ZAYN 2 278
Zaz 2 279
Zazula 2 280
ZUTOMAYO 2 281
张远 2 282
水槽 2 283
結束バンド 2 284
馬嘉祺 2 285
马嘉祺 2 286
1 287
¥ellow Bucks 1 288
¥S 1 289
113 1 290
2007快乐男声全国13强 1 291
2115 1 292
24. Avēnija 1 293
2Scratch 1 294
49 Winchester 1 295
50 Cent 1 296
A’Vista 1 297
A Tergo Lupi 1 298
A1 x J1 1 299
Aavani Malhar 1 300
Absolute Valentine 1 301
AC/DC 1 302
Aden Foyer 1 303
Aditya Rikhari 1 304
Aimer 1 305
Air 1 306
Aitch 1 307
Aiyana-Lee 1 308
AJ Tracey 1 309
Akon 1 310
Alan Menken 1 311
Alanis Morissette 1 312
Alex Isley 1 313
Alexander 23 1 314
Aliyah’s Interlude 1 315
Alon Eder 1 316
alt-J 1 317
Alvvays 1 318
Aly & AJ 1 319
America 1 320
Amesaki Annainin 1 321
Aminé 1 322
Amit Trivedi 1 323
Amon Düül II 1 324
Anastacia 1 325
Andrew Lambrou 1 326
Andrzej Zaucha 1 327
Anurag Kulkarni 1 328
Arca 1 329
Arcade Fire 1 330
ARIETE 1 331
ASAP Ferg 1 332
Ashbeck 1 333
ASIAN KUNG-FU GENERATION 1 334
ASKII 1 335
Aslak Trude 1 336
Austin Farwell 1 337
Avi Kaplan 1 338
Aviv Guedj 1 339
AYYBO 1 340
Babe Rainbow 1 341
Baby B3ns 1 342
Baby Keem 1 343
badkarma 1 344
Bahamas 1 345
Barbra Streisand 1 346
Barney Artist 1 347
Bathe 1 348
Bazart 1 349
Beach Weather 1 350
Belmondawg 1 351
Ben Platt 1 352
benny blanco 1 353
Benny Sings 1 354
berlioz 1 355
Bérurier Noir 1 356
beslik 1 357
betcover!! 1 358
Białas 1 359
Big Thief 1 360
Birkir Blær 1 361
BLANCO 1 362
BLK ODYSSY 1 363
BLØF 1 364
Blue Öyster Cult 1 365
Bob Marley & The Wailers 1 366
Bobby Womack 1 367
Bon Iver 1 368
Bon Jovi 1 369
Bonobo 1 370
Boostereo 1 371
Boulevard Depo 1 372
boy pablo 1 373
boygenius 1 374
Boys Town Gang 1 375
Brandon Nembhard 1 376
Britney Spears 1 377
BROCKHAMPTON 1 378
Bruce Springsteen 1 379
BTS 1 380
Buntspecht 1 381
C. Tangana 1 382
Cabu 1 383
Cage The Elephant 1 384
CAKE 1 385
Calah Lane 1 386
Calinacho 1 387
Campanella 1 388
Captain Beefheart & His Magic Band 1 389
Cardi B 1 390
Carlos Rafael Rivera 1 391
Carly Simon 1 392
Carpenters 1 393
Cast - Frozen 1 394
Celeste 1 395
Central Cee 1 396
Certified Trapper 1 397
Chance Peña 1 398
Charles Trenet 1 399
Cherry Pit 1 400
CHIC 1 401
Chico Buarque 1 402
Chikinki 1 403
Chill Bump 1 404
Chopstick Sisters 1 405
Chris Brown 1 406
Chris Isaak 1 407
Chris Young 1 408
Christian Reindl 1 409
Cigarettes After Sex 1 410
Clouseau 1 411
Cody Chesnutt 1 412
Cohen 1 413
1 414
Cold Dew 1 415
Combichrist 1 416
Cotto 1 417
Creedence Clearwater Revival 1 418
Crystal Castles 1 419
Culture Club 1 420
Current Blue 1 421
Damsonegongbang 1 422
Daniel Caesar 1 423
Daniel Deluxe 1 424
Daniel Di Angelo 1 425
Dao Bandon 1 426
David Bowie 1 427
David Guetta 1 428
David Kushner 1 429
DC 1 430
DECAP 1 431
Declan McKenna 1 432
Deep Chills 1 433
Depeche Mode 1 434
Desiigner 1 435
Dhibu Ninan Thomas 1 436
dialE 1 437
Dido 1 438
Dinusie 1 439
Dionne Warwick 1 440
Disclosure 1 441
DJ Danifox 1 442
dj gummy bear 1 443
DJ Maphorisa 1 444
DJ Miki 1 445
Djordan 1 446
Doremisie 1 447
Dotan 1 448
Duran Duran 1 449
DURDN 1 450
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The research

Column

Introduction

Songs that we listen to every day do not only stimulate our mood, and help as get through our daily chores. They tell us stories, to which we can relate, describe events that we dream of, or simply circulate around words that define our life routines. The lyrics play an integral part in our daily musical sphere. While there is no doubt that the character of the lyrics differs between artists, as well as genres, a question emerges, whether their sophistication has a certain correlation on the “other” side. Namely, if the listeners’ own musical sophistication, and other characteristics (such as background, English proficiency) are connected to the character of the lyrics of the songs they listen to most often. The studies on the musical and language sophistication have not yet been developed extensively, with most of the research focusing on more general relations within the area of music culture - e.g. the one between the music listening and cultural adaptation in host states (provide citation). Thus, our research had as its objective the provision of a valuable point for discussion.

Methodology

The research was based on a survey, to which we collected 55 complete responses. While the number of the total participants was almost twice as high, lack of response to one of the questions already precluded them from being taken into account. To gather the data that would allow us to measure numerous variables connected to the musical sophistication and language, we have asked the participants of our survey some general questions concerning their personal information - their age group, native language, English proficiency, as well as home country. Further, they were asked about their musical sophistication, with the use of inquiries selected from Gold MSI. Additionally, we asked each participant to share a link to their Spotify On Repeat playlist. In order to study the data, we have used Spotify and Genius APIs, and a number of indices for the measurement of the readability of the lyrics - Flesch Reading Ease, Flesch Kincaid Grade Level, Gunning Fog Index, Smog Index, Automated Readability Index, Coleman Liau Index, Linsear Write, Dale Chall Readability formula, and McAlpine EFLAW Readability Score. Due to the limitations of these indices, the only songs we could measure were English-language songs.

Limitations

  1. Number of participants

As mentioned earlier, we have managed to collect 55 complete responses. This sample group cannot be assessed as sufficient to claim, that scores presented are representative of the population at large. Therefore, for the further studies, not constrained by a short time limit, we would advise to collect responses from a big representative group.

  1. Language

This limitation is explicitly connected to the previous one. We have managed to gather only a few responses from certain language groups. Further, some language/language proficiency groups have not been represented in the research at all. This undermines the final assessment of the score (with regard to these groups). A more international approach is necessary – this should be obtainable if the research is conducted by an international group (which was the case here), but within a longer time frame. As regards a one of the language proficiency groups – “low English proficiency” – survey might be translated, in order to make it possible for this group to be represented in the research.

  1. English lyrics

Due to limitations of various indices that we have used to assess the readability of the song lyrics, we were able to only include English songs from participants’ playlists. Thus, we had to filter out many of the songs that they often listened to. Creating an index that assesses readability of the word regardless of the language may provide for future research in this area, that would not be limited to English songs. It may also lead to interesting outcomes, for example – listeners with lower English proficiency, but high musical sophistication, may be found to listen to less “readable” songs (but in non-English language).

  1. Capturing language sophistication

Lastly, it should also be noted that readability that metrics of readability may not be a good representation of the language sophistication. They capture the difficulty of the words and omit their meaning. Many indices are also optimized to work with sentences, which many songs don’t have. This made them not usable in the study.

Column

Discussion and conclusions

While we have managed to gather responses from people of various nationalities, the majority of the respondents were coming from the age group 18-23 (over 80%). Further, their level of English proficiency was also largely limited to “advanced” and “native”, with none of the participants claiming a “low” level of proficiency.

As regards the second part of the survey, the participants were characterized by a wide range of musical sophistication. While the lowest score amounted to 39, the highest was 115.

Nevertheless, regardless of these above mentioned differences, the outcome of our research provided, that there was little, if any correlation between the character of the lyrics of the songs we listen to every day, and the musical sophistication of the listener. The results that were the closest to showing a certain level of positive relation between these two categories related to the sophistication score, and the average sentiment of the lyrics (correlation of 0.162). However, this correlation needs to be approached critically, as the sample group cannot be regarded as representative of a bigger population, with the p-value of 0.24.

The readability metrics have also been compared to the declared English proficiency of the participants. Likewise, it can be said that there appears to be no relation between the readability score and the English proficiency. The box plots of the main page could suggest that listeners with moderate proficiency are listening to less readable lyrics, however this result is likely the outcome of a low number of data points in this group (5).

The results of the research indicate, that contrary to a belief that one might have, musical sophistication, as well as English language proficiency do not clearly correlate with the character of the lyrics of the (English) songs we listen to. Nevertheless, more extensive and longer research, especially one that would include songs with non-English lyrics, might provide shifts in these scores, and show that in some cases the correlation is more apparent.

Further reading

Rosebaugh, Caleb, and Lior Shamir. “Data Science Approach to Compare the Lyrics of Popular Music Artists.” Unisia, 3 July 2022, pp. 1–26, https://doi.org/10.20885/unisia.vol40.iss1.art1. Accessed 14 Dec. 2022.

Shamir, Lior. “UDAT: Compound Quantitative Analysis of Text Using Machine Learning.” Digital Scholarship in the Humanities, vol. 36, no. 1, 13 Mar. 2020, pp. 187–208, https://doi.org/10.1093/llc/fqaa007. Accessed 11 Nov. 2022.

Müllensiefen D, Gingras B, Musil J, Stewart L (2014) “The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population.” PLoS ONE 9(2): e89642. https://doi.org/10.1371/journal.pone.0089642

Baker, D. J., Ventura, J., Calamia, M., Shanahan, D., & Elliott, E. M. (2020). “Examining musical sophistication: A replication and theoretical commentary on the Goldsmiths Musical Sophistication Index.” Musicae Scientiae, 24(4), 411-429. https://doi.org/10.1177/1029864918811879

Slevc, L. R., & Miyake, A. (2006). “Individual Differences in Second-Language Proficiency: Does Musical Ability Matter? Psychological Science”, 17(8), 675-681. https://doi.org/10.1111/j.1467-9280.2006.01765.x

Lipovetsky, S. “Readability Indices Structure and Optimal Features.” Axioms 2023, 12, 421. https://doi.org/10.3390/ axioms12050421

Baker, David John ; Ventura, Juan ; Calamia, Matthew ; Shanahan, Daniel ; Elliott, Emily M. „Examining musical sophistication: A replication and theoretical commentary on the Goldsmiths Musical Sophistication Index.”Musicae scientiae, 2020, Vol.24 (4), p.411-429.

Article reviews

Column

Comparing lyrics

Cultural adoption

Music listening and cultural adaptation: How different languages of songs affect Chinese international students’ uses of music and cultural adaptation in the United States

Ignacy Walus

The relation between music and language is without a doubt a complex one. On the one hand, language has a great influence on music, affecting its style, and having profound impact on its listeners. On the other hand, music may be considered to influence acquiring language skills. Furthermore, this may lead to the role of music – and its language – in adopting cultural customs as well. Such relation, between the fields of music listening and cultural adaptation, is studied by Jia and Koku in their article on the role of music in the integration of Chinese students in the United States. In this paper, I will review their work, by summarizing and evaluating their research.

The article at issue is based on four research questions, that together aim to examine what role English and Chinese music plays in the process of acculturation of Chinese international students in the United States. Firstly, the authors try to find if various genres of music influence different uses of music – for mood regulation, and identity building, among others. Secondly, they examine how the language of music – Chinese or English – affects these various uses. Thirdly, they study the implications of the language of music Chinese students listen to every day on their cultural adaptation. Lastly, they discuss whether this effect differs by uses of music.

The findings of the article Jia and Koku are based on self-reported research the authors conducted among the Chinese international students in the United States. The final sample group consisted of 256 students from three universities. The survey contained questions concerning not only the language or type of music Chinese students listened to every day, and its primary use, but also questions relating to demographic facts (on gender, length of stay in the US), and asking how frequently specific music was listened to by them.

The results presented by the authors of the article reveal numerous interesting facts concerning the relation between everyday music and cultural adaptation. The research shows, that while the type of music genre has no significant effect on different use of music, listening to music in either Chinese or English predicts its certain usage. While English music primarily serves for entertainment and identity building, Chinese music is used for negative mood management. This leads to the authors’ argument, that for bilingual listeners, songs in their native language serve for this very purpose of mood management – on the other hand, the non-native language songs have entertaining goal. Further, the authors observe, that since English songs positively affect Chinese students’ acculturation, they emerge as a means to help them “forge new identities”. Jia and Koku find that frequent listening to English songs for the goal of identity building is pivotal in developing Chinese students’ integration in the American society – but needs to be further strengthened by their own identification with the music itself. Importantly, the authors also stress the limitations to their study that concern the small number of participants in the survey, and the question of applicability of its results to the other groups of international students. They point out that further questions on this research remain to be examined.

“Music listening and cultural adaptation…” emerges as an important article in the field of studying music listening and its impact on individuals. The research conducted by Jia and Koku contributes to it by displaying not only how listening to music can be used for cultural adaptation in a new country, but also by showing how important the role of the language of this music itself is. Furthermore, the article studies a topic that is relatable to most of the international students that move abroad to a country that is to some degree culturally different that their home state. It shows that integration is conducted not only externally – via contacts with friends, local culture – but also internally, through our own personal activities, such as everyday music listening. The article has an additional value, if we consider the fact, that we live in a world where mobility takes precedence, and thus, the “necessity” of cultural adaptation is common. And as it is shown, music listening might play a more and more significant role in this process. The article needs to be further praised for its relevant and interesting findings – e.g. pointing out that Chinese songs primarily serve for negative mood management of students. While some of the other results might be considered as not surprising, they are nevertheless crucial to examine the process of students’ acculturation.

However, the article also has significant drawbacks. The first, and the most obvious one, is the small size of the sample group. While the authors are conscious of this limitation, as they point out at the end that the results shall not be generalized for the whole population of Chinese students in America, numerous findings and arguments stated throughout the text appear as very general and applicable to not only Chinese, but many other international students concerned with cultural adaptation. More emphasis should have been put on this limitation, to provide clear “barriers” to the applicability of the findings. Further, the design of the research can be considered as limited, which may be to the detriment of the accuracy of the results. Namely, the findings to the first research question concerning relations between genres and uses of music are based on a small number of categories of genres – these are collapsed to five with the highest number of responses (Chinese pop, Chinese others, American pop, American others, and music from other countries). While this gives more clarity, fragmenting these categories may provide for different results – and genres may indeed be found to influence various uses of music. Lastly, it needs to be pointed out, that the study largely concerns English music, which dominates the music industry worldwide. Therefore, the findings that listening to non-native music predicts use of music for identity building should be approached carefully. English music may indeed influence the integration in English-speaking countries. However, it is rather doubtful that it serves the same purposes in non-English speaking states, where, due to its omnipresence, it would be still one of the most (if not the most) popular genres.

The article by Jia and Koku is without a doubt an important starting point in a debate over relation between music and language, and music’s role in cultural assimilation. As outlined, it contains drawbacks, that should be dealt with in the future academic works. And as the topic is far from being exhausted, further studies on it shall only be a matter of time.

Bibliography 1. Jia, Fei, Emmanuel Koku. “Music listening and cultural adaptation: How different languages of songs affect Chinese international students’ uses of music and cultural adaptation in the United States”. Journal of International and Intercultural Communication 13. No. 4 (2020). 291-308.

Tonality and speech

Article Review: Co-Variation of Tonality in the Music and Speech of Different Cultures

Tomer Shor

Music is often considered to be the universal language, despite the fact that the traditional music and underlying musical theory of various cultures differs greatly. The differences between traditional Western and Eastern music, especially in regard to pitch and tonal usage, have long been recognized, both in scholarly literature and music theory. Similarly, pitch usage also varies greatly between languages, which, accordingly, might be grouped as “tonal” or “non-tonal” languages. Whereas in tonal languages the pitch contours affect and convey the meaning of the spoken word, non-tonal languages share no such features, and variations of pitch or relative level do not typically alter the meanings of words.

With these differences in mind, Han et al. sought to answer the following question – is there a connection between the two phenomena? Is there a correlation between the tonal characteristics of the music and the speech patterns of specific cultures? In order to investigate their hypothesis – that said correlation does indeed exist – the authors examined 50 traditional melodies of tone language cultures – Mandarin, Thai, and Vietnamese – and 50 comparable melodies from non-tone language cultures – American, French and German. Additionally, the authors commissioned the recording of monologues read by native speakers of all six languages, instructing them to speak as if they were engaged in normal conversation. The melodies and speech recordings were then analyzed in terms of the frequency of slope reversals (changes in pitch direction), and the size of the melodic intervals used. The results of said analyses were subsequently used to compare the frequency of slope reversals: the music of tonal languages to the music of non-tonal languages, and the speech of tonal languages to that of non-tonal ones. A similar, four-way comparison was conducted with the results of the melodic interval analysis.

The results of the research seem to support the authors’ initial hypothesis. Both the speech and the music of tonal languages displayed higher frequencies of slope reversals compared to those of non-tonal languages; similarly – while the distribution of interval size between tonal and non-tonal languages was not as skewed as the distribution of slope reversals – there was a substantial difference in distribution between the two languages groups, with speech and music displaying parallel tendencies. These results lead the authors to conclude that there exists an intimate relationship between language and music, manifested in tonal distinction between the music of tonal and non-tonal languages, that parallels the distinctions between the languages.

However, in their discussion of the results, the authors reach conclusions that seem unsupported by the data – namely, that the variations in aesthetic preferences between cultures is explained in terms of biology. While this is a plausible explanation, nowhere in the article are those biological differences explicated. Nor is it the only possible explanation: while biological differences – either innate or brought about by historical and material conditions – certainly might explain the variations in tonal usage, it is also possible that a musical Sappir-Whorf theory of sorts is at play here. Sappir-Whorf theory, or the linguistic relativity hypothesis, claims that the language an individual speaks fundamentally affects and alters their perception of the world. A similar claim might be made about music and language – that the language of a culture inherently affects the type of music produced by that culture and its properties – and/or vice versa. In fact, this claim is supported by the authors’ data, and the characteristics of the relation between language and music within each culture is discussed in the article, e.g., in relation to scale. Traditional Eastern music tends to employ pentatonic (five-tone) scales, while traditional Western music tends to use heptatonic (seven-tone) scales, leading Han et al. to posit that the difference in scale use, rather than being simply an aesthetic one, reflects the desire to mirror the melodic intervals used in speech. This explanation, however, is at odds with the authors’ conclusion regarding biological differences – if the cause of tonal differences in language is a biological one, why would the tonal differences in music not stem from the same source? In other words – is it the language that shapes the music, the music that shapes the language, or rather a third variable (biological or otherwise) that shapes both?

This criticism is further grounded in the methods of the study: in order to mitigate possible cross-cultural contamination regarding music, the authors specifically chose melodies that pre-date 1900, preferably by hundreds of years. Yet, for the speech analysis part, the authors commissioned recordings by current-day language speakers. Thus, the two sets of comparison materials are not equivalent, with the musical dataset being comprised of tunes that are hundreds of years old – tunes that native speakers are probably familiar with, and, after decades of being in the zeitgeist of each respective culture, could have had an influence on certain cultural and language tendencies. Similarly, the languages themselves might have undergone certain changes during that period – speech patterns change over them, invariably affecting the tendencies of slope reversals and the interval size of spoken language. For the comparison to be fair and accurate, it ought to either feature contemporary tunes, or historical recordings from the periods of the tunes selected – although that, obviously, creates its own set of complications.

Despite these points of contention, Han et al. provide a valuable addition to the study of music and language. Speech and music are both essential modes of expression, shared by all people across all cultures. It is precisely because of this that it is so important to investigate the differences between and within these modes of expression, and to try and highlight the similarities. While many musical cultures and their subsequent use of scales appears to converge around the harmonic scale , indicating a biological underpinning to our musical preferences – differences between musical cultures are still abundant, suggesting further mechanisms that influence aesthetic outcomes. Han et al.’s contribution is a first step in trying to unearth these mechanisms, focused on language. While further research is warranted – both in comparisons between tonal and non-tonal languages, and of tonal languages to other tonal languages and non-tonal languages to other non-tonal languages – this article makes clear that the interplay between music and language exists, is empirically observable – and has a lot to teach us about ourselves and our cultures.

Bibliography Han S, Sundararajan J, Bowling DL, Lake J, Purves D (2011) Co-Variation of Tonality in the Music and Speech of Different Cultures. PLoS ONE 6(5): e20160. https://doi.org/10.1371/journal.pone.0020160 Gill KZ, Purves D (2009) A Biological Rationale for Musical Scales. PLoS ONE 4(12): e8144. doi:10.1371/journal.pone.0008144 Bowling DL, Sundararajan J, Han S, Purves D (2012) Expression of Emotion in Eastern and Western Music Mirrors Vocalization. PLoS ONE 7(3): e31942. https://doi.org/10.1371/journal.pone.0031942

Example lyrics with scores

Row

High readability

Artist: Little Big

Song: Go Bananas

Linsear Write Formula: 3.17

Sentiment: 0.64

Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Now (Oop! Oop!)

Oh let me see you go ba-na-nas (Oop! Oop!)

Oh let me see you go

I’m gonna nuts right now

I’m gonna, I’m gonna fruits right now (Oop!)

I’m gonna lose ma mind

I’m gonna, I’m gonna lose my mind (Oop!)

Go bananas, be like Banana Man

Go bananas, be like Banana Man

Go bananas, be like Banana Man

Go bananas, be (Oop! Oop!) like Banana Man

Bana-nana-nana-na na-na

Bana-nana-nana-na na-na

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Bana-nana-nana-na na-na

Bana-nana-nana-na na-na

Bana-nana-nana-na na-na

Ya! I’m Banana Man

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Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

I’m Banana Man

Banana

Banana

I’m Banana, I’m Banana Man (Banana)

I’m Banana, I’m Banana Man (Banana)

I’m Banana, I’m Banana Man (Banana)

I’m Banana (I’m Banana, I’m Banana Man)

Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Bana-nana-nana-na na-na (Ay!)

Bana-nana-nana-na na-na (Now!)

Bana-nana-nana-na na-na

Ya! I’m Banana Man

Low readability

Artist: Lana Del Rey

Song: Ultraviolence

Linsear Write Formula: 58

Sentiment: 0.18

He used to call me DN

That stood for Deadly Nightshade

’Cause I was filled with poison

But blessed with beauty and rage

Jim told me that, he hit me and it felt like a kiss

Jim brought me back, reminded me of when we were kids

With his ultraviolence

Ultraviolence

Ultraviolence

Ultraviolence

I can hear sirens, sirens

He hit me and it felt like a kiss

I can hear violins, violins

Give me all of that ultraviolence

He used to call me poison

Like I was poison ivy

I could have died right there

’Cause he was right beside me

Jim raised me up, he hurt me but it felt like true love

Jim taught me that, loving him was never enough

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With his ultraviolence

Ultraviolence

Ultraviolence

Ultraviolence

I can hear sirens, sirens

He hit me and it felt like a kiss

I can hear violins, violins

Give me all of that ultraviolence

We could go back to New York

Loving you was really hard

We could go back to Woodstock

Where they don’t know who we are

Heaven is on Earth

I would do anything for you, baby

Blessed is this union

Crying tears of gold like lemonade

I love you the first time, I love you the last time

Yo soy la princesa, comprende mis white lines

’Cause I’m your jazz singer and you’re my cult leader

I love you forever, I love you forever

With his ultraviolence (Lay me down tonight)

Ultraviolence (In my linen and curls)

Ultraviolence (Lay me down tonight)

Ultraviolence (Riviera Girls)

I can hear sirens, sirens

He hit me and it felt like a kiss

I can hear violins, violins

Give me all of that ultraviolence

Low sentiment

Artist: Coldplay

Song: Amsterdam

Linsear Write Formula: 50

Sentiment: -0.24

Come on, oh, my star is fadin’

And I swerve out of control

And if I, oh, if I’d only waited

I’d not be stuck here in this hole

Come here, oh, my star is fadin’

And I swerve out of control

And I swear I waited and waited

I’ve got to get out of this hole

But time is on your side

It’s on your side now

Not pushin’ you down and all around

It’s no cause for concern

Come on, oh, my star is fadin’

And I see no chance of release

And I know I’m dead on the surface

But I am screamin’ underneath

And time is on your side

It’s on your side now

Not pushin’ you down and all around, oh

It’s no cause for concern

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Stuck on the end of this ball and chain

And I’m on my way back down again

Stood on a bridge, tied to a noose

Sick to the stomach

You can say what you mean, but it won’t change a thing

I’m sick of the secrets

Stood on the edge, tied to the noose

And you came along and you cut me loose

You came along and you cut me loose

You came along and you cut me loose

High sentiment

Artist: Troye Sivan

Song: Rush

Linsear Write Formula: 28

Sentiment: 0.43

I feel the rush

Addicted to your touch

Big communication, tell me what you want

Translate your vibration, let your body talk to me

Baby love, if you wanna show me what

You’ve been schemin’ up, if you wanna (Let go)

Trust the simulation, don’t you let it break

Every stimulation, promise I can take

What you wanna give? Boy, you better show me what

You’ve been schemin’ up

You got my heartbeat racin’

My body blazin’

I feel the rush

Addicted to your touch

Oh, I feel the rush

It’s so good, it’s so good

I feel the rush

Addicted to your touch

Oh, I feel the rush

It’s so good, it’s so good

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So good when we slow gravity, so good

It’s so good, it’s so good

Breathe one, two, three, take all of me, so good

It’s so good, it’s so good

Pass your boy the heatwave, recreate the sun

Take me to the feeling, boy, you know the one

Kiss it when you’re done, man, this shit is so much fun

Pocket rocket gun

You got my heartbeat (Heartbeat) racin’ (Racin’)

My body blazin’

I feel the rush

Addicted to your touch

Oh, I feel the rush

It’s so good, it’s so good

I feel the rush

Addicted to your touch

Oh, I feel the rush

It’s so good, it’s so good

So good when we slow gravity, so good

It’s so good, it’s so good

Breathe one, two, three, take all of me, so good

It’s so good, it’s so good

It’s so good, it’s so good

It’s so good, it’s so good

Chosen metric correlations

Column

Correlations of chosen metrics

Word frequencies

Column

Word frequency in the songs included in the project (Top 500, no stopwords)

Column

Top 500 words (no stopwords)

freq
like 6976
one 6058
will 4483
said 4378
know 4302
now 3932
dont 3631
love 3442
just 3363
time 3234
can 3209
see 3060
man 3031
get 2861
say 2671
day 2526
back 2519
feat 2494
got 2429
take 2375
never 2349
come 2338
make 2242
might 2231
good 2137
yeah 2123
also 2099
king 2026
two 2019
night 2016
made 2012
life 1952
way 1936
thou 1885
world 1872
people 1851
upon 1808
want 1788
even 1778
first 1765
little 1739
well 1722
may 1710
youre 1699
let 1663
cant 1627
right 1616
thee 1583
says 1572
1559
still 1528
came 1525
baby 1522
give 1519
cause 1518
ill 1511
tell 1508
head 1496
men 1490
eyes 1469
without 1386
bloom 1381
every 1376
old 1350
long 1349
away 1343
thats 1318
till 1302
think 1298
look 1254
much 1250
heart 1229
hand 1216
thy 1207
place 1196
another 1189
great 1183
face 1182
call 1170
home 1163
feel 1160
must 1158
new 1146
left 1137
court 1134
ever 1129
mind 1112
god 1111
went 1108
last 1086
black 1081
need 1081
house 1081
wanna 1080
’s 1075
going 1068
put 1068
money 1068
shall 1068
things 1065
three 1055
young 1043
nothing 1039
around 1034
heard 1028
many 1026
girl 1022
name 1016
jordan 1000
father 996
aint 993
keep 989
set 982
days 975
sharrkan 960
women 939
told 935
better 932
found 927
leave 926
allah 924
woman 914
city 909
thing 907
though 900
saw 892
end 889
always 885
lil 884
shit 873
find 871
hands 871
took 870
mother 865
boy 864
light 861
bad 855
high 853
big 841
gonna 837
years 832
hear 817
part 817
son 805
body 801
asked 796
thought 793
side 791
far 789
death 784
wont 778
fire 775
white 774
called 768
saying 768
something 766
hes 765
theres 759
really 758
whole 744
best 732
yet 730
since 729
words 726
zau 720
fuck 708
street 705
case 700
dead 693
used 691
ive 686
knew 685
brother 681
done 680
gave 675
run 667
stephen 666
next 661
stop 658
hath 657
room 648
bitch 641
enough 638
water 637
iran 633
real 632
almakan 630
work 629
friends 629
alone 628
bed 623
soul 622
gone 622
hold 622
live 620
answered 620
mam 620
hard 619
everything 616
door 613
rest 612
talk 609
full 608
die 608
country 606
round 604
friend 601
coming 600
army 599
lost 598
fall 597
morning 597
stay 596
soon 595
592
whose 592
behind 589
person 581
use 576
replied 569
ooh 567
moment 566
seen 564
company 564
red 562
matter 561
boys 560
whether 560
state 559
word 559
among 558
hell 557
together 555
lord 554
turned 551
music 548
bring 546
mrs 544
air 543
war 543
times 538
turn 536
brought 536
show 532
year 531
forth 531
looking 530
dark 530
lady 529
sent 529
thus 528
free 526
truth 520
others 520
second 518
power 517
blood 516
land 516
art 514
ask 513
play 513
help 509
shes 509
goes 507
mean 504
true 502
officers 501
open 496
making 495
wall 495
act 493
anything 493
wait 491
comes 491
reason 490
hey 488
fine 486
dad 486
indeed 485
voice 483
felt 481
certain 481
mme 481
child 480
watch 480
makes 478
reached 478
four 475
different 474
believe 473
order 473
sun 473
blue 472
thousand 471
fear 470
feet 470
ten 469
point 467
mine 464
read 464
save 463
police 463
fell 463
ground 460
quoth 460
school 459
near 459
hundred 458
yes 456
wish 456
taken 455
force 454
girls 453
none 453
tells 453
miss 450
care 449
else 446
less 444
sure 444
children 443
didnt 443
ready 441
five 440
march 439
ceased 439
sleep 438
don’t 434
hair 434
past 432
wrong 431
began 431
given 429
gotta 427
seemed 427
public 427
law 425
try 424
already 423
course 423
means 421
fight 419
return 418
song 418
walk 417
fact 416
dance 415
change 415
cry 413
line 413
able 413
within 413
towards 412
arms 411
’m 411
lets 410
cyrus 410
pay 409
quite 409
speak 407
along 407
outside 407
please 406
rich 406
rather 406
omar 405
meet 403
several 403
present 401
poor 400
number 399
officer 397
remix 396
feeling 395
human 395
least 393
youll 392
road 392
getting 392
kind 392
river 392
nigga 391
half 388
gold 388
stand 387
close 387
family 385
chance 385
lay 385
dream 384
384
lie 383
cut 382
earth 382
sweet 382
however 382
rose 381
sound 380
often 380
party 379
break 378
horse 378
niggas 378
wazir 378
wife 377
sea 376
hope 375
dawn 374
living 372
taking 371
story 370
remember 370
holy 369
general 369
daughter 368
malachy 368
top 367
desire 367
kill 366
sometimes 366
form 366
become 366
bin 366
whats 365
almost 364
passed 364
talking 363
answer 363
cold 363
eye 362
theyre 361
sight 360
wanted 360
kiss 358
future 354
green 353
met 353
summer 352
tonight 352
fpd 351
move 350
heaven 350
empress 350
kid 349
mouth 349
tears 348
car 344
’re 344
sorry 343
known 343
drink 340
age 339
hit 339
brown 338
forever 338
west 338
sister 338
sky 336
later 335
stood 335
forward 334
looked 334
including 334
private 333
inside 332
seeing 332
guermantes 332
held 328
start 327
john 325
single 325
whatever 325
across 324
lot 324
cried 323
perhaps 323
small 322
send 322
longer 322
everybody 321
knows 319
kings 319
front 317
lines 317
pass 316
either 315
late 314
zoe 314
sir 313
states 311
understand 310
game 310
takes 309
kept 309
sit 309
ferguson 309
madeleine 307
today 305
hot 305
beat 305

Word frequencies (not included)

Column

Word frequency in the songs not included in the project (Top 500)

Column

Top 500 words

freq
nie 1492
que 1130
und 1072
jak 946
się 811
die 806
les 715
ich 683
des 542
der 510
pas 482
also 480
might 468
sie 447
dans 408
like 404
nijisanji 378
jest 360
pour 349
nicht 341
non 336
das 330
że 327
mnie 326
cest 322
comme 316
sich 306
wie 303
che 297
qui 294
den 293
mit 292
ein 269
auf 265
ist 258
sur 239
ale 237
project 234
ça 230
tak 221
mais 220
war 217
une 211
yeah 208
mam 206
est 197
plus 195
von 193
son 191
doch 187
ten 186
con 181
mon 181
quand 164
czy 160
virtuareal 160
tylko 159
jai 159
una 157
dem 155
daß 154
już 153
avec 151
jestem 149
149
mir 147
als 147
wenn 146
tout 141
jeden 141
mes 138
nous 135
het 134
wir 130
por 129
par 129
hololive 128
ona 127
como 127
eine 126
für 124
ihm 124
los 122
dla 121
nur 121
kiedy 119
119
baby 119
sind 119
jsuis 119
mich 118
bien 117
aus 116
man 114
не 114
faire 112
mein 110
tes 109
gdy 108
immer 108
לא 107
hatte 107
seine 107
fait 106
106
tym 105
tous 104
nos 103
103
dich 103
hier 103
dit 103
per 102
danse 102
auch 101
wieder 101
mai 100
nee 100
dwa 99
teraz 99
chcę 98
rien 98
ton 98
elle 97
production 96
jej 95
sein 94
więc 93
znowu 92
noch 92
niet 92
moi 91
mój 89
oder 89
pod 88
dziś 87
vor 87
vie 87
del 85
alles 85
ihn 85
ses 85
את 85
wiem 84
come 84
przez 83
las 82
ihr 80
denn 80
toi 80
אני 80
quon 79
aber 78
rap 77
да 77
love 76
bei 76
trop 76
nic 75
faut 75
nuit 75
לי 75
nas 74
être 74
на 74
bez 73
coś 73
nach 73
quoi 73
potem 73
bin 72
een 72
get 72
bam 72
qué 71
sont 71
tego 71
każdy 70
70
aux 70
noc 70
react 70
hmm 69
hab 69
liebe 69
זה 69
einen 69
kurwa 68
wiesz 68
tam 67
même 66
cette 66
było 66
chce 65
ktoś 65
może 65
sei 65
kann 65
ils 65
toute 65
cię 65
65
मेरे 65
मौला 65
64
robię 64
mal 64
ciągle 64
will 64
bis 64
dat 64
wat 64
64
63
63
kommt 63
wilhelm 63
van 62
weil 62
ces 62
lui 62
sleep 62
nigdy 62
nanashiinku 62
ciebie 61
życie 61
one 60
pero 60
sobie 60
tattica 60
weg 60
suis 60
quil 60
nun 60
durch 60
цак 60
ohohoh 59
solo 59
bad 59
einem 59
cuando 58
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maar 58
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wszystko 57
sonne 57
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masz 56
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dobrze 56
mieć 56
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dadadada 56
chyba 56
sam 56
live 56
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żeby 55
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sous 55
toujours 55
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jme 55
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zeit 54
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life 53
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hajs 52
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algorhythm 50
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bow 49
temps 49
sommes 49
bitch 49
nowy 49
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gdzie 48
chcą 48
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nacht 48
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nikt 47
sono 47
dir 47
ganz 47
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mia 46
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pixela 46
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czuję 45
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